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An Incremental Algorithm for Handling Qualitative Spatio-Temporal Information

Authors Zhiguo Long , Qiyuan Hu , Hua Meng , Michael Sioutis

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Author Details

Zhiguo Long
  • School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China
Qiyuan Hu
  • School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China
Hua Meng
  • School of Mathematics, Southwest Jiaotong University, Chengdu, China
Michael Sioutis
  • Faculty of Information Systems and Applied Computer Sciences, Universität Bamberg, Germany

Funding

This work was supported by the National Natural Science Foundation of China (61806170), the Humanities and Social Sciences Fund of Ministry of Education (18XJC72040001), and the National Key Research and Development Program of China (2019YFB1706104).

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Zhiguo Long, Qiyuan Hu, Hua Meng, and Michael Sioutis. An Incremental Algorithm for Handling Qualitative Spatio-Temporal Information. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.COSIT.2022.5

Abstract

In this paper, we present an online (incremental) algorithm for checking the satisfiability of qualitative spatio-temporal data, with direct implications to other fundamental knowledge representation and reasoning problems for such data, like the problems of deductive closure and redundancy removal. In particular, qualitative data come in the form of human-like, symbolic, descriptions such as "region x contains or overlaps region y", which are abundant in the Web of Data. Our approach is also able to maintain, to some extent, any sparse graph structure that may be inherent in the data, i.e., it acts parsimoniously and only tries to infer new information when needed for soundness and completeness. To this end, we complement our practical algorithm with certain theoretical results to assert its correctness and efficiency. A subsequent evaluation with publicly available large-scale real-world and random datasets against the state of the art, shows the interest and promise of our method.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Temporal reasoning
  • Computing methodologies → Spatial and physical reasoning
Keywords
  • Online algorithm
  • qualitative data
  • spatio-temporal reasoning
  • satisfiability checking
  • knowledge representation and reasoning

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References

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